A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization

Xinyu Hua, Lu Wang

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Abstract
We study the problem of domain adaptation for neural abstractive summarization. We make initial efforts in investigating what information can be transferred to a new domain. Experimental results on news stories and opinion articles indicate that neural summarization model benefits from pre-training based on extractive summaries. We also find that the combination of in-domain and out-of-domain setup yields better summaries when in-domain data is insufficient. Further analysis shows that, the model is capable to select salient content even trained on out-of-domain data, but requires in-domain data to capture the style for a target domain.
Anthology ID:
W17-4513
Volume:
Proceedings of the Workshop on New Frontiers in Summarization
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Lu Wang, Jackie Chi Kit Cheung, Giuseppe Carenini, Fei Liu
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
100–106
Language:
URL:
https://aclanthology.org/W17-4513
DOI:
10.18653/v1/W17-4513
Bibkey:
Cite (ACL):
Xinyu Hua and Lu Wang. 2017. A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization. In Proceedings of the Workshop on New Frontiers in Summarization, pages 100–106, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization (Hua & Wang, 2017)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/W17-4513.pdf